Systems and methods for identifying medical image acquisition parameters

ABSTRACT

Systems and methods are disclosed for identifying image acquisition parameters. One method includes receiving a patient data set including one or more reconstructions, one or more preliminary scans or patient information, and one or more acquisition parameters; computing one or more patient characteristics based on one or both of one or more preliminary scans and the patient information; computing one or more image characteristics associated with the one or more reconstructions; grouping the patient data set with one or more other patient data sets using the one or more patient characteristics; and identifying one or more image acquisition parameters suitable for the patient data set using the one or more image characteristics, the grouping of the patient data set with one or more other patient data sets, or a combination thereof.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.61/925,947 filed Jan. 10, 2014, the entire disclosure of which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally toimaging and related methods. More specifically, particular embodimentsof the present disclosure relate to systems and methods for identifyingmedical image acquisition parameters.

BACKGROUND

Imaging may be critical in many commercial settings. Users of imagingmay include any range of professionals or consumers. As one example,medical doctors, technicians, and/or other individuals trained toacquire medical images may all employ imaging to make patient caredecisions. Medical imaging may include radiography, computed tomography(CT), magnetic resonance imaging (MRI), fluoroscopy, single-photonemission computed tomography (SPECT), positron emission tomography(PET), scintigraphy, ultrasound, as well as specific techniques (e.g.,echocardiography, mammography, intravascular ultrasound, andangiography).

By way of example, one application of medical imaging is the diagnosisand treatment of coronary artery disease, which may produce coronarylesions in the blood vessels providing blood to the heart, such as astenosis (abnormal narrowing of a blood vessel). Patients suffering fromcoronary artery disease may experience a restriction of blood flow tothe heart and resulting chest pain, referred to as chronic stable anginaduring physical exertion or unstable angina when the patient is at rest.A more severe manifestation of disease may lead to myocardialinfarction, or heart attack.

Patients suffering from chest pain and/or exhibiting symptoms ofcoronary artery disease may be subjected to one or more noninvasivetests that may provide some indirect evidence relating to coronarylesions. For example, noninvasive tests may include electrocardiograms,biomarker evaluation from blood tests, treadmill tests,echocardiography, SPECT, and PET. Anatomic data may be obtainednoninvasively using coronary computed tomographic angiography (cCTA).cCTA may be used for imaging of patients with chest pain. For example,cCTA may involve using CT technology to image the heart and the coronaryarteries following an intravenous infusion of a contrast agent.

Although use of imaging may be pervasive, the image acquisition processstill has limitations. For example, in the medical context, trade-offsmay lie between capturing an image of a quality high enough to provideinformation to make a medical decision (e.g., a diagnosis), while at thesame time, minimizing risk to a patient (e.g., from radiation exposure)and resources used for the image acquisition. Therefore, a desire mayexist to identify or anticipate image acquisition parameters that mayproduce images of requisite quality, while limiting traditionaldrawbacks, e.g., radiation exposure and resource usage associated withquality imaging. In other words, a desire may exist for determining orobtaining optimized image acquisition parameters, for instance, prior toobtaining an image or scan.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for identifying image acquisition parameters. Onemethod includes: receiving a patient data set including one or morereconstructions, one or more preliminary scans or patient information,and one or more acquisition parameters; computing one or more patientcharacteristics based on one or both of one or more preliminary scansand the patient information; computing one or more image characteristicsassociated with the one or more reconstructions; grouping the patientdata set with one or more other patient data sets using the one or morepatient characteristics; and identifying one or more image acquisitionparameters suitable for the patient data set using the one or more imagecharacteristics, the grouping of the patient data set with one or moreother patient data sets, or a combination thereof.

In accordance with another embodiment, a system for identifying imageacquisition parameters comprises: a data storage device storinginstructions for identifying image acquisition parameters; and aprocessor configured for: receiving a patient data set including one ormore reconstructions, one or more preliminary scans or patientinformation, and one or more acquisition parameters; computing one ormore patient characteristics based on one or both of one or morepreliminary scans and the patient information; computing one or moreimage characteristics associated with the one or more reconstructions;grouping the patient data set with one or more other patient data setsusing the one or more patient characteristics; and identifying one ormore image acquisition parameters suitable for the patient data setusing the one or more image characteristics, the grouping of the patientdata set with one or more other patient data sets, or a combinationthereof.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for identifying imageacquisition parameters is provided. The method includes: receiving apatient data set including one or more reconstructions, one or morepreliminary scans or patient information, and one or more acquisitionparameters; computing one or more patient characteristics based on oneor both of one or more preliminary scans and the patient information;computing one or more image characteristics associated with the one ormore reconstructions; grouping the patient data set with one or moreother patient data sets using the one or more patient characteristics;and identifying one or more image acquisition parameters suitable forthe patient data set using the one or more image characteristics, thegrouping of the patient data set with one or more other patient datasets, or a combination thereof.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network foridentifying image acquisition parameters, according to an exemplaryembodiment of the present disclosure.

FIG. 2 is a block diagram of an exemplary method of a training phase fordeveloping a model that can be used to predict image quality of primaryimaging data, according to an exemplary embodiment of the presentdisclosure.

FIG. 3 is a block diagram of an exemplary method of a production phasefor finding image acquisition parameters to achieve a desired imagequality, based on the training phase model and circumstances associatedwith primary imaging data, according to an exemplary embodiment of thepresent disclosure.

FIG. 4 is a block diagram of an exemplary method of a specific trainingphase for developing a model using various types of preliminary scans orimages, according to an exemplary embodiment of the present disclosure.

FIG. 5 is a block diagram of an exemplary method of a specificproduction phase for predicting image quality of medical images,according to an exemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Imaging may be critical in many commercial settings. In particular,medical imaging may be ubiquitous in providing healthcare. Still, theremay exist several limitations, both to information that imaging mayprovide, as well as limitations in image acquisition. Regardinginformation limitations, medical imaging information from non-invasivediagnostic imaging for coronary artery disease may come with well-knownlimitations in defining the anatomy of lesions in the coronary arteries.For instance, methods are still being refined for obtaining accuratedata relating to coronary lesions, e.g., size, shape, location,functional significance (e.g., whether the lesion impacts blood flow),etc. based on imaging. Regarding image acquisition limitations, medicalimaging may involve weighing a priority of obtaining a high qualityimage, against for instance, resources expended and/or patient radiationexposure.

Image acquisition parameters (e.g., x-ray tube voltage, x-ray beamfilter, detector configuration, exposure time, frames per unit time,frames per trigger, device-specific settings, slice thickness, scanmode, rotation time, etc.) may be initialized prior to acquiring animage. The quality of images may vary based on an imaging subject (e.g.,a patient with high BMI may require parameters distinct from parametersof a patient with low BMI, or patients with different cardiac output mayrequire different scan times, etc.), a particular image acquisitioninstrument (e.g., physical dimensions of optical components, outputx-ray characteristics, a helical CT scanner in a particular room of aspecific hospital, etc.), operator characteristics (e.g., variousprocesses or idiosyncrasies associated with people or entities operatinginstruments for image acquisition, etc.), and/or preliminary images(e.g., scout images) that may be used to prescribe, check, and/orcalibrate the image acquisition. The parameters may impact the qualityof an acquired image, meaning an optimal set of acquisition parametersmay contribute to producing a high quality image. However, acquisitionparameters are generally population-based or parameters dictated bygeneral or standard imaging protocols. Acquisition parameters are nottailored for a particular patient's specific anatomy or specific anatomybeing imaged and the particular task at hand. For example, imaging dosefor imaging small features with low contrast density variation (e.g.,plague) may differ from suitable imaging dose for imaging large featureswith high contrast density variation (e.g., myocardial perfusion). Imageacquisition may not take into account such distinctions that may improveresultant imaging. Thus, a desire exists to identify image acquisitionparameters that may produce a high quality image.

The present disclosure is directed to systems and methods foridentifying medical image acquisition parameters, and, moreparticularly, to systems and methods for iteratively optimizing imageacquisition and reconstruction parameters to produce high qualitymedical images, including images involved in cardiac CT imageacquisition. As described above, the quality of medical images may beinfluenced by one or more of: population-based or standard imageacquisition parameters, the imaging subject, the particular imageacquisition instrument, and preliminary images. The present disclosureis directed to using historical data and/or machine learning techniquesto train one or more computing systems to predict an optimal set ofimage acquisition parameters that may produce a high quality image whilelimiting unfavorable imaging conditions. In addition to an exemplarygeneral embodiment, the present disclosure describes embodimentsrelating to predicting image acquisition parameters based on preliminaryscans, including scout, calcium scoring scans, operator characteristics,and/or contrast timing scans. As an extension, predicting imageacquisition parameters may also entail predicting an image quality basedon image quality produced by past images acquired using the same imageacquisition parameters.

In one embodiment, the disclosed techniques for identifying andoptimizing medical image acquisition parameters may be applicable to andused in connection with methods for estimating patient-specific bloodflow characteristics, such as those methods described in U.S. Pat. No.8,315,812 issued Nov. 20, 2012, to Charles A. Taylor, the entiredisclosure of which is incorporated by reference herein. Althoughcertain embodiments of the present disclosure are described, forpurposes of example, with respect to the diagnosis and treatment ofcoronary artery disease, the systems and methods described herein areapplicable to the prediction of optimal sets of image acquisitionparameters in relation to any field of medical imaging.

Specifically, the present disclosure may be directed to a trainingphase, where a system may learn and/or create a model that may distillrelationships between three sets of information: patientcharacteristics, acquisition parameters, operator characteristics and/orimage quality. The training phase may include processing data associatedwith a plurality of individuals to develop an understanding of howvarious patient characteristics and acquisition parameters may impactimage quality. By extension, the present disclosure may be furtherdirected to a production phase of determining acquisition parametersthat may help produce images of a particular quality, with respect to aparticular patient for the target imaging application. For example, theproduction phase may include finding acquisition parameters that mayyield an optimal image quality for the desired imaging application, inlight of characteristics specific to the particular patient, acquisitioninstrument, and/or desired target acquisition parameter. In some cases,a target acquisition parameter may define a priority either in imagequality and/or acquisition (e.g., where a priority may includeminimizing radiation exposure). In addition to outputting optimizedacquisition parameters, the production phase may further includeproducing high quality images based on the optimized acquisitionparameters. In some cases, the systems and methods described may bepertinent to acquisition of cardiac CT images.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for identifying image acquisitionparameters, according to an exemplary embodiment. Specifically, FIG. 1depicts a plurality of physicians 102 and third party providers 104, anyof whom may be connected to an electronic network 100, such as theInternet, through one or more computers, servers, and/or handheld mobiledevices. Physicians 102 and/or third party providers 104 may create orotherwise obtain images of one or more patients' anatomy. The physicians102 and/or third party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, etc. Physicians 102 and/or third partyproviders 104 may transmit the anatomical images and/or patient-specificinformation to server systems 106 over the electronic network 100.Server systems 106 may include storage devices for storing images anddata received from physicians 102 and/or third party providers 104.Server systems 106 may also include processing devices for processingimages and data stored in the storage devices.

FIG. 2 is directed to a general embodiment for a method of training amodel to learn associations between an imaging subject (e.g., apatient), acquisition parameters (including parameters used for imagingand/or an image acquisition instrument/device), operatorcharacteristics, and preliminary scans (e.g., images of the imagingsubject acquired using the acquisition parameters). FIG. 3 is directedto a general embodiment for a method of producing an output of imageacquisition parameters suitable for a particular patient, based on themodel of associations from the method in FIG. 2. In some embodiments,the parameters output of FIG. 3 may be taken as recommendations forimage acquisition parameters. More specifically, the parameters outputfrom the method and FIG. 3 may include parameters for producing qualityimages under designated conditions including imaging subjects and imageacquisition instruments or devices. In other words, the method of FIG. 3may provide image acquisition parameters to optimize image quality forcapturing a specified image subject using a specified image acquisitiondevice, based on image quality of images of the same (or similar)imaging subject(s), using the same (or similar) image acquisitioninstrument(s)/device(s), and for the same operator. FIG. 4 is directedto a specific embodiment of a training phase of developing models,wherein the embodiments describe training models based on various typesof preliminary scans or images. The exemplary preliminary scans mayinclude scout scans, calcium scans, contrast timing scans, previousscans of a single or selected modality (e.g., a modality that is thesame as an imaging modality to be used for imaging anatomy of theparticular patient), and/or previous scans of different modalities. FIG.5 is directed to a specific embodiment of a production phase of usingthe models to determine image acquisition parameters for producing aquality medical image, for a given imaging subject and image acquisitiondevice.

FIG. 2 is a block diagram of an exemplary method 200 of a training phasefor developing a model that can be used to predict image quality ofprimary imaging data, based on preliminary scans, operatorcharacteristics, acquisition parameters, and reconstructions, accordingto an exemplary embodiment. The method of FIG. 2 may be performed byserver systems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network100. In some embodiments, the predicted image quality may include imagequality of primary imaging data, e.g., predicting the quality of a cCTAscan, prior to acquiring the scan.

The training phase essentially builds an understanding of associationsbetween patient characteristics, acquisition parameters, and imagequality. Method 200 may train a system to associate patientcharacteristics (from preliminary scans and/or patient information) withacquisition parameters and image quality. Specifically, in oneembodiment, method 200 may include a system (e.g., including serversystems 106), receiving multiple electronic representations of data setscomprised of: one or more reconstructions of medical images, one or morepreliminary scans (e.g., scout images) and/or patient information,acquisition parameters, operator characteristics, acquisition deviceinformation, etc. For example, the electronic representations may betransmitted via a hard drive, cloud architecture, etc. For each dataset, the system may compute image and patient characteristics from thereceived preliminary scan images and/or patient information. The systemmay further compute, for each data set, several image characteristics ofthe received reconstructions. For example, image characteristics mayinclude measures of global image quality and image quality in localregions of the received reconstructions. For each acquisition devicetype then, the system may assign one or more clusters. The assignmentsmay be based on the patient characteristics computed from thepreliminary scans in each data set. In some embodiments, the clustersmay refer to groupings of data, e.g., bundles or networks of the datasets based on similarities in patient characteristics associated witheach data set, as well as data set associations with the acquisitiondevice type. The block diagram of FIG. 2 shows method 200 with a focuson how the system may process each data set to develop a model providinginsight into resultant image characteristics from various acquisitionand patient variables.

In one embodiment, step 201 may include receiving a training data setfor each of a plurality of individuals. As previously discussed, the setmay include reconstructions of medical images, patient scout images andinformation, and acquisition parameters. Next, step 203 may includecomputing and/or finding image and patient characteristics for each dataset. For instance, step 203 may include determining metadata tags foreach set, where the tags may characterize image and patientcharacteristics derived from the received patient scout images andinformation. Next, step 205 may include computing image characteristicsof processed images in the received set. For example, step 205 mayinclude computing image quality characteristics of the reconstructionsin the received set. Once image and patient characteristics for the sethave been determined, step 207 may be initiated. Step 207 may includeassociating the set with acquisition parameters. For example, step 207may include determining several acquisition device types and providingidentification or retrieval information for the set based on arespective acquisition device (or acquisition device type) used toproduce the set. Furthermore, step 207 may include placing the set in agrouping based on its associated acquisition parameters. For example,step 207 may include placing the set into a cluster based on thecomputed patient characteristics (e.g., from step 203). Several clustersmay be arranged in terms of their associations to acquisition parameters(e.g., including an acquisition device type). The model resulting frommethod 200 may include sets of data, defined by patient characteristics,image characteristics, operator characteristics, and acquisition data.Once parameters for acquisition are defined (e.g., specifying anacquisition device type), the model may output patient characteristicsand optionally, an expected or predicted resultant image quality.Alternately, the model may also output acquisition parameters andcorresponding image quality for the target imaging application, if giveninformation regarding patient characteristics. Such outputs are based onthe training data sets and associations between patient characteristics,image characteristics, operator characteristics, and acquisition dataachieved by exemplary training method 200. FIG. 3 includes furtherdetail regarding the outputs, or use, of method 200.

FIG. 3 is a block diagram of an exemplary method 300 of a productionphase for finding image acquisition parameters to achieve a desiredimage quality, given specific patient characteristics, according to anexemplary embodiment. The method of FIG. 3 may be performed by serversystems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network100. Method 300 may be executed on a system (e.g., server systems 106),where the system for method 300 may be the same system, or a differentsystem that may perform method 200. In some embodiments, method 300 maybe automated, where a system running method 300 may include an automatedimage acquisition parameter system.

In some embodiments, once the system has learned to associate patientand operator characteristics with acquisition parameters and imagequality (e.g., from method 200), method 300 may be used to predictoptimal image scan parameters to produce images of acceptable imagequality, in light of provided patient characteristics. In oneembodiment, step 301 may include receiving a data set. In someinstances, this data set may pertain to a particular patient and be usedas a data set for production (e.g., image acquisition production orimage acquisition parameter production). The set may include one or morepreliminary images and/or patient information, acquisition parameters(e.g., acquisition data information, operator characteristics), etc. Inone embodiment, step 303 may include receiving a designated acquisitionparameter. For example, the designated parameter may be a parameter tooptimize. In the following discussion, “optimizing” the parameter willbe described as “minimizing” the parameter. For example, if thedesignated parameter is “radiation exposure” or “noise”, optimizing theparameter may entail minimizing radiation exposure in acquisition ornoise in an image, respectively. While other forms of optimizingparameters exist, the discussion below will focus on the exemplaryembodiment where “optimizing” refers to “minimizing”. In one embodiment,step 303 may include receiving a desired target minimum image qualityscore. In some cases, at least the set, designated acquisitionparameters and/or the target minimum image quality score may be receivedin the form of an electronic representation (e.g., on a hard drive,cloud architecture, etc.).

In one embodiment, step 305 may include computing several patientcharacteristics from the preliminary images and/or patient information(e.g., received in step 301). Step 305 may include identifyingparameters of patient characteristics computed in the training phase(e.g., method 200), and determining values for those same patientcharacteristics in the preliminary images and/or patient informationreceived from step 301. Step 305 may further include identifyingparameters of operator characteristics and determining operatorcharacteristics associated with image acquisition and/or quality.

In one embodiment, step 307 may include receiving information specifyingan acquisition device type. For example, the acquisition device type maybe a type of device available in a certain setting for acquiring images.Alternately or in addition, the acquisition device type may be based onthe data set (e.g., received acquisition information or acquisitiondevice information from step 301).

In one embodiment, step 307 may then include determining a clusterrelated to the patient data set (e.g., received in step 301). Forexample, step 307 may include assigning the data set to a clusterincluding (training) data sets associated with an acquisition devicetype. The cluster may be one of several clusters including setsdetermined to be associated with the acquisition device type (e.g.,during the training phase shown through method 200). In other words,clusters may be comprised of one or more training sets, grouped based onacquisition device type and patient information. The assignment may bebased on patient characteristics (e.g., computed in step 305). Theassignment may further be based on operator characteristics. In someinstances, the assigned cluster may include at least one training setwith an image quality score exceeding the designated minimum imagequality score (e.g., from step 303). The image quality score of thetraining set may be based on image characteristics computed, forinstance, in step 205 of method 200.

In one embodiment, step 309 may include determining a training setwithin the assigned cluster that has an optimal designated acquisitionparameter. As previously discussed, “optimal” may mean a minimum orlowest value for a designated acquisition parameter. For example, atraining set with an optimal designated acquisition parameter of noise,may have the lowest noise out of all the training sets within theassigned cluster. Determining the training set may include calculatingvalues for the designated acquisition parameter, for each of thetraining sets within the assigned cluster. Furthermore, determining thetraining set with the optimal designated acquisition parameter mayinclude identifying or selecting a training data set, out of thetraining data sets in the assigned cluster, based on the calculatedvalues. In one embodiment, step 311 may include the system outputtingacquisition parameters associated with the training set from step 309.In some instances, the parameters may be output to an electric storagemedium (e.g., hard drive, screen display, etc.). Step 313 may includeacquiring a scan (e.g., cCTA) based on the output acquisitionparameters.

FIG. 4 is a block diagram of an exemplary method 400 of a training phasefor developing a model to predict image quality of primary imaging datafrom acquisition parameters, reconstructions, and various types ofpreliminary scans (e.g., scout scans, calcium scans, and/or contrasttiming scans). The method of FIG. 4 may be performed by server systems106, based on information, images, and data received from physicians 102and/or third party providers 104 over electronic network 100. In oneembodiment, step 401 may include a system receiving training data sets,for each of a plurality of individuals, including one or morereconstructions of medical images, one or more preliminary scans and/orpatient information, acquisition parameters, acquisition deviceinformation, operator characteristics, etc. The operator characteristicsmay include, for instance, operator identifiers (e.g., an operatoridentification code), time between scans, quality of previous scansassociated with the operator (or operator identification code), etc. Insome cases, operators may produce images at a range of efficiency orquality. Method 400 may account for such a range in receiving operatorcharacteristics as part of the training data sets.

In some instances, the training data sets may be in the form ofelectronic representations (e.g., on a hard drive, cloud architecture,etc.). In some cases, step 401 may include the system receiving multipleelectronic representations, including multiple electronicrepresentations for each training data set, multiple electronicrepresentations for multiple training data sets, or a combinationthereof.

In one embodiment, steps 403 a-407 may include the system performingseveral computations for each of the received training data sets. Forexample, steps 403 a-403 c may include variations on a step where thesystem may compute several measures globally, depending on the receivedtype of preliminary scan(s). As previously stated, the preliminaryscan(s) may include a preliminary scout scan, a preliminary calciumscan, and/or a preliminary contrast timing scan.

In one embodiment involving a preliminary scout scan, step 403 a mayinclude the system computing measures of image quality and patientcharacteristics globally in the preliminary scout scan, where the globalmeasures may include at least one of the following measures: imageresolution, contrast level, noise level, contrast to noise ratio, motionor blurring, partial volume effect or blooming, beam hardening (e.g., ascalculated by a separation of high-enhancing material andwater-enhancing material by Hounsfield unity intensity on the inputimage), medication provided at the time of image acquisition, heart rateat the time of image acquisition, presence of anatomic abnormalities,patient anatomy, presence of implanted devices or prior surgeries, etc.In an embodiment involving a preliminary calcium scan, step 403 b mayinclude the system computing several measures of image quality globallyin the preliminary calcium scan and reconstructions. The global measuresmay include at least one of the following metrics: image resolution,contrast level, noise level, contrast to noise ratio, motion orblurring, partial volume effect or blooming, beam hardening (e.g., ascalculated by a separation of high-enhancing material andwater-enhancing material by Hounsfield unity intensity on the inputimage), medication provided at the time of image acquisition, heart rateat the time of image acquisition, presence of anatomic abnormalities,slice thickness, misregistration or misalignment, phase of acquisition,etc. In an embodiment involving a preliminary contrast timing scan, step403 c may include the system computing several measures of image qualityglobally in the preliminary contrast timing scan and reconstructions.The global measures for step 403 c may include at least one of thefollowing measures: image resolution, contrast level, noise level,contrast to noise ratio, motion or blurring, partial volume effect orblooming, beam hardening (e.g., as calculated by a separation ofhigh-enhancing material and water-enhancing material by Hounsfield unityintensity on the input image), medication provided at the time of imageacquisition, heart rate at the time of image acquisition, presence ofanatomic abnormalities, patient anatomy, presence of implanted devicesor prior surgeries, misregistration or misalignment, phase ofacquisition, etc.

Steps 405 a-405 c may include the system computing global and localimage quality measures with respect to a primary scan. For example, step405 a may include the system computing several measures of image qualityglobally and in local regions of at least one reconstructed medicalimage defined by each coronary centerline in available reconstructionsof a primary scan (e.g. a coronary computed tomography (CT) angiographyscan). In some instances, the reconstructed medical image may be definedby anatomical landmarks (e.g., each centerline in all availablereconstructions). In other instances, the reconstructed medical imagemay be defined by centerlines in a portion of the reconstructions.Coronary centerlines may be defined by running coronary arterycenterline tree extraction algorithms. The global and local measures mayinclude at least one of: image resolution, slice thickness, number ofscanner slices, missing slices or missing data, length of ascendingaorta in image, field of view, noise level, contrast to noise ratio,misregistration or misalignment as detected by abrupt changes in imageintensity, motion or blurring as characterized by edge width or edgedetection, partial volume or blooming as detected by a dramatic increasein image intensity, beam hardening (as calculated by separation ofhigh-enhancing material and water-enhancing material by Hounsfield unitintensity on an input image), estimated sensitivity of a localizedregion of a coronary vessel to variation in a physiological simulation,estimated radiation dose (e.g., implied from Digital Imaging andCommunications in Medicine (DICOM) header or calculated from the scan,etc. In some cases, step 405 a may be executed in conjunction with anembodiment of method 400 involving a preliminary scout scan.

In one embodiment, step 405 b may include the system computing severalmeasures of image quality globally and in local regions of at least onereconstructed medical image defined by each coronary segment inavailable reconstructions of a primary scan. The reconstructed medicalimage may be defined by coronary segments in all, or a portion of theavailable reconstructions. Coronary segments may be identified byrunning coronary artery centerline tree extraction algorithms. Theglobal and local measures may include at least one of: image resolution,slice thickness, number of scanner slices, missing slices or missingdata, length of ascending aorta in image, field of view, noise level,contrast to noise ratio, misregistration or misalignment as detected byabrupt changes in image intensity, motion or blurring as characterizedby edge width or edge detection, partial volume or blooming as detectedby a dramatic increase in image intensity, beam hardening (as calculatedby separation of high-enhancing material and water-enhancing material byHounsfield unit intensity on an input image), estimated sensitivity of alocalized region of a coronary vessel to variation in a physiologicalsimulation, presence of implanted devices or prior surgeries, etc. Insome cases, step 405 b may be executed in conjunction with an embodimentof method 400 involving a calcium scoring scan. For the embodiment ofmethod 400 including a preliminary calcium score scan, step 405 c mayfurther include computing patient characteristics (e.g., from thepreliminary scans and/or patient information of step 401).

In one embodiment, step 405 c may include the system computing severalmeasures of image quality globally and in local regions of at least onereconstructed medical image defined by each coronary centerline inavailable reconstructions of a primary scan. The reconstructed medicalimage may be defined by coronary centerlines in all, or a portion of theavailable reconstructions. As stated previously, coronary centerlinesmay be identified by running coronary artery centerline tree extractionalgorithms. The global and local measures may include at least one of:image resolution, slice thickness, number of scanner slices, missingslices or missing data, length of ascending aorta in image, field ofview, noise level, contrast to noise ratio, misregistration ormisalignment as detected by abrupt changes in image intensity, motion orblurring as characterized by edge width or edge detection, partialvolume or blooming as detected by a dramatic increase in imageintensity, beam hardening (as calculated by separation of high-enhancingmaterial and water-enhancing material by Hounsfield unit intensity on aninput image), estimated sensitivity of a localized region of a coronaryvessel to variation in a physiological simulation, presence of implanteddevices or prior surgeries, interpretability score assessing theinterpretability of any given coronary segment with respect to a givenlocal image quality, etc. In some cases, step 405 c may be executed inconjunction with an embodiment of method 400 involving a contrast timingscan.

In one embodiment, step 407 may include assigning an image quality scorebased on the computed image quality measures from steps 405 a, 405 b,and/or 405 c. For example, step 407 may include assigning an imagequality score pertaining to each data set received in step 401. Step 407may further include assigning image quality score(s) based on a singlemeasure of image quality, multiple measures of image quality, particularcombinations of measures for global and specified local regions, etc. Inother words, step 407 may include determining image quality scoresassociated with a subset of the received data sets (e.g., from step401), a subset of quality metrics, a subset of image regions oranatomies, etc.

In one embodiment, step 409 may include computing a single integratedimage quality score. In one embodiment, step 409 may include computingthe integrated image quality score based on the score(s) calculated instep 407. The computation may include, for example, calculating a mean,median mode, minimum value, maximum value, range, weighted mean, or anycombination thereof.

In one embodiment, step 411 may include receiving image acquisitionparameters, including an acquisition device type. Acquisition devicesmay be characterized by features including, at least, the following: CTvendor, patient characteristics (e.g., bariatric patients), scan mode(e.g., helical, axial, electrocardiography (ECG)-gated, etc.), pitch inhelical mode or table feed in axial mode, rotation time, x-ray tubevoltage, x-ray tube current, x-ray beam filter, x-ray beam collimation,detector configuration (e.g., 128 slices×0.7 mm), automatic exposurecontrol, reconstruction kernel, etc. For embodiments including apreliminary scout scan and/or contrast timing scan, acquisition devicesmay further be characterized by an iterative reconstruction technique.

In one embodiment, step 413 may include assigning each data set to oneor more clusters associated with each acquisition device type. In someembodiments, the assignments to one or more clusters may be based onfeatures including patient characteristics and measurements or metricscomputed from scout scans, calcium scoring scans, contrast timing scansfor each training data set (e.g., from steps 403 a, 403 b, and 403 c,respectively). In one embodiment, assignments in step 413 may beperformed using any clustering algorithms, for example, a k-meansclustering algorithm. For instance, the k-means clustering algorithm maybe used to optimize assignments to clusters.

FIG. 5 is a block diagram of an exemplary method 500 of a productionphase for predicting image quality of new coronary computed tomographyangiograph (cCTA) images, according to an exemplary embodiment. Themethod of FIG. 5 may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 100.

As described above, the training phase of method 400 may includetraining a system to associate preliminary scout scans, calcium scoringscans, and/or contrast timing scans, device characteristics, andacquisition parameters, with local and global image quality metricsalong each coronary segment. For example, method 400 may include amachine learning algorithm to train the system to recognize or learn theassociations. Based on these associations, method 500 may anticipateimage quality based on information given regarding, for instance, devicecharacteristics, operator characteristics (e.g., from an operator ID),or patient information. Method 500 may further include using newacquired images as feedback to optimize or further hone the predictivecapability for future scans. For example, information associated withnewly acquired images may serve as an input or training data set toinform the optimization of image acquisition.

Specifically, step 501 may include receiving a data set for a particularpatient, including patient information, acquisition parameters,acquisition device information, operator characteristics, and apreliminary scout image, calcium scoring image, or contrast timingimage. The data set may pertain to a particular patient. In someembodiments, the acquisition device information may correspond toacquisition device information defined in a related training phase(e.g., from step 411 or 413 of method 400). In one embodiment, step 503may include receiving a designated acquisition parameter. This parametermay be a parameter that method 500 may be used to optimize (orminimize). For example, step 503 may include receiving “estimatedradiation dose” as the designated acquisition parameter.

In one embodiment, step 503 may further include receiving a designatedtarget minimum image quality score. For example, the minimum imagequality score may refer to an integrated image quality score (e.g.,similar to the integrated image quality score described for step 409 ofmethod 400). In some embodiments, the data set, designated acquisitionparameter, and/or designated target minimum may be received via anelectronic representation (e.g., on a hard drive, cloud architecture,etc.).

Step 505 may include computing patient characteristics and/or patientinformation based on the preliminary scans or images. In someembodiments, at least a portion of the patient characteristics and/orinformation computed in step 505 may include patient characteristicsand/or patient information used in the training phase (e.g., method400). In one embodiment, step 507 may include specifying an acquisitiondevice type (e.g., a device type for which training data exists and/or adevice type respective of the received patient data set from step 501).Step 507 may include using the patient characteristics (e.g., from step505) and/or operator characteristics, to assign the received data setfrom step 501 (and associated patient) to a cluster determined in thetraining phase. In some embodiments, step 507 may including making theassignment such that the received data set is assigned to a cluster thatincludes at least one training set with an image quality score exceedingthe designated minimum image quality score (e.g., from step 503).

Step 509 may include determining the training set, within the assignedcluster, with sufficient image quality scores. Such a training set maybe associated with the optimal designated acquisition parameter. Forthis embodiment of method 500, the designated acquisition parameter is“estimated radiation dose.” Therefore, step 509 may include identifyinga training set associated with a minimum radiation dosage to a patientin acquiring the image(s).

In one embodiment, step 511 may include determining and/or retrievingacquisition and reconstruction parameters associated with the trainingset determined in step 513. For example, step 511 may include outputtingthe acquisition and reconstruction parameters of the determined trainingset in an electronic storage medium (e.g., hard drive, screen display,etc.). Furthermore, method 500 may include step 513 of acquiring animage or scan based on the acquisition parameters from step 511. Forexample, step 513 may include producing a primary cCTA scan based on theoutput acquisition parameters. In other words, the output acquisitionand/or reconstruction parameters may serve as suggested or recommendedparameters for future image acquisitions and/or reconstructions.Presumably, the image or scan acquired using the output acquisitionparameters (e.g., from step 513) may have an image quality similar tothe training data set image quality associated with the outputacquisition parameters.

In some embodiments, at least information from the data set, designatedtarget image quality score, or acquisition device type, (from steps 501,503, or 507, respectively) may comprise a request. The request mayprompt steps 511 or 513, thus causing the system to output optimizedimage acquisition or initiate an image acquisition based on theoptimized image acquisition parameters, for example.

As previously discussed, image acquisition (e.g., from step 513) mayfurther serve as feedback on the predicative ability of the modeldeveloped by the training phase. Images acquired using output optimizedimage acquisition parameters may be expected to be of a similar qualityto the image quality of the training data set associated with the outputimage acquisition parameters, at least with respect to the designatedacquisition parameter. For the image or scan of step 513 to serve asfeedback, step 513 may further include determining a data set associatedwith the image or scan. The data set may include one or morereconstructions, one or more preliminary scans and/or patientinformation, and one or more acquisition parameters, consistent with thepreviously described training data sets.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A computer-implemented method of determiningpredicted image quality scores of one or more images for use inoperating a medical imaging scanner, the method comprising: identifyingone or more data acquisition device types; determining one or more datasets associated with the one or more data acquisition device types,wherein each data set of the one or more data sets includes one or morepatient characteristics and an image quality score respective to thedata set; grouping the one or more data sets based on similaritiesbetween the one or more patient characteristics respective to each ofthe one or more data sets; receiving a selected data acquisition devicetype; identifying a selected data set associated with the selected dataacquisition device type; and determining a predicted image quality scorefor an image produced by the selected data acquisition device type,based on the grouping of the one or more data sets.
 22. The method ofclaim 21, further comprising: determining a patient characteristic of aselected patient; and determining the predicted image quality scorefurther based on the patient characteristic of the selected patient andthe grouping of the one or more data sets.
 23. The method of claim 21,further comprising: determining one or more recommended imageacquisition parameters associated with the predicted image qualityscore.
 24. The method of claim 21, further comprising: receiving anacquisition parameter or image quality score; and further identifyingthe selected data set, based on the received acquisition parameter orthe received image quality score.
 25. The method of claim 21, furthercomprising: identifying the selected data set based on one or moreimaging operator characteristics, one or more image characteristics, orone or more effects of imaging, wherein the one or more effects ofimaging includes radiation exposure.
 26. The method of claim 21, furthercomprising: computing the one or more patient characteristics and theimage quality score respective to each of the one or more data sets. 27.The method of claim 21, wherein each of the one or more data setsincludes one or more reconstructions of medical images or one or morepatient preliminary images.
 28. The method of claim 21, furthercomprising: initiating or instructing production of an image based onthe predicted image quality score.
 29. A system for determiningpredicted image quality scores of one or more images for use inoperating a medical imaging scanner, the system comprising: a datastorage device storing instructions for determining predicted imagequality scores of one or more images for use in operating a medicalimaging scanner; and a processor configured to execute the instructionsto perform a method including: identifying one or more data acquisitiondevice types; determining one or more data sets associated with the oneor more data acquisition device types, wherein each data set of the oneor more data sets includes one or more patient characteristics and animage quality score respective to the data set; grouping the one or moredata sets based on similarities between the one or more patientcharacteristics respective to each of the one or more data sets;receiving a selected data acquisition device type; identifying a dataset associated with the selected data acquisition device type; anddetermining a predicted image quality score for an image produced by theselected data acquisition device type, based on the grouping of the oneor more data sets.
 30. The system of claim 29, wherein the system isfurther configured for: determining a patient characteristic of aselected patient; and determining the predicted image quality scorefurther based on the patient characteristic of the selected patient andthe grouping of the one or more data sets.
 31. The system of claim 29,wherein the system is further configured for: determining one or morerecommended image acquisition parameters associated with the predictedimage quality score.
 32. The system of claim 29, wherein the system isfurther configured for: receiving an acquisition parameter or imagequality score; and further identifying the selected data set, based onthe received acquisition parameter or the received image quality score.33. The system of claim 29, wherein the system is further configuredfor: identifying the selected data set based on one or more imagingoperator characteristics, one or more image characteristics, or one ormore effects of imaging, wherein the one or more effects of imagingincludes radiation exposure.
 34. The system of claim 29, wherein thesystem is further configured for: computing the one or more patientcharacteristics and the image quality score respective to each of theone or more data sets.
 35. The system of claim 29, wherein each of theone or more data sets includes one or more reconstructions of medicalimages or one or more patient preliminary images.
 36. The system ofclaim 29, wherein the system is further configured for: initiating orinstructing production of an image based on the predicted image qualityscore.
 37. A non-transitory computer readable medium for use on acomputer system containing computer-executable programming instructionsfor determining predicted image quality scores of one or more images foruse in operating a medical imaging scanner; the method comprising:identifying one or more data acquisition device types; determining oneor more data sets associated with the one or more data acquisitiondevice types, wherein each data set of the one or more data setsincludes one or more patient characteristics and an image quality scorerespective to the data set; grouping the one or more data sets based onsimilarities between the one or more patient characteristics respectiveto each of the one or more data sets; receiving a selected dataacquisition device type; identifying a data set associated with theselected data acquisition device type; and determining a predicted imagequality score for an image produced by the selected data acquisitiondevice type, based on the grouping of the one or more data sets.
 38. Thenon-transitory computer readable medium of claim 37, the method furthercomprising: determining a patient characteristic of a selected patient;and determining the predicted image quality score further based on thepatient characteristic of the selected patient and the grouping of theone or more data sets.
 39. The non-transitory computer readable mediumof claim 37, the method further comprising: determining one or morerecommended image acquisition parameters associated with the predictedimage quality score.
 40. The non-transitory computer readable medium ofclaim 37, the method further comprising: initiating or instructingproduction of an image based on the predicted image quality score.